Patient monitoring systems and methods

Abstract

Techniques disclosed herein relate to monitoring changes in conditions of multiple individuals in areas. In some embodiments, a patient monitoring queue may be established ( 504 ) that includes a plurality of patients in an area ( 104, 304 ) such as a waiting room. The area may be capturable by vital sign acquisition camera(s) ( 276. 376, 476 ) mounted in or near the area. Updated vital sign(s) may be unobtrusively acquired ( 510 ) by the vital sign acquisition camera(s) from a given patient selected ( 506 ) from the patient monitoring queue. Based on the updated vital sign(s) and prior vital signs acquired previously from the given patient, deterioration of the given patient may be detected ( 512 ). Output may be provided ( 514 ) alerting medical personnel of the deterioration of the given patient.

What is claimed is: 
     
       1. A computer-implemented method, comprising:
 acquiring one or more initial vital signs from each of a plurality of patients; 
 determining a patient acuity measure associated with each of the plurality of patients based on the acquired one or more initial vital signs from each of the plurality of patients; 
 establishing, by one or more processors, a patient monitoring queue that includes the plurality of patients, the plurality of patients being located in an area that can be captured by one or more vital sign acquisition cameras; 
 unobtrusively acquiring, by one or more of the vital sign acquisition cameras, one or more updated vital signs from a given patient selected from the patient monitoring queue; 
 determining, by one or more of the processors, an updated patient acuity measure for the given patient based on the one or more updated vital signs unobtrusively acquired by the one or more vital sign acquisition camera from the given patient using a machine learning model, 
 detecting, by one or more of the processors, based on a comparison of the updated patient acuity measure for the given patient and the previously determined patient acuity measure for the given patient, deterioration of the given patient; 
 updating, by one or more of the processors, the machine learning model by writing new trained weights for at least one module of the machine learning model; and 
 providing, by one or more of the processors, output alerting medical personnel of the deterioration of the given patient, 
 wherein the one or more vital signs cameras acquire the one or more updated vital signs by applying image detection algorithms, and 
 wherein the one or more updated vital signs comprise one or more of blood pressure, pulse, glucose level, SO 2 , photoplethysmogram, respiration rate, temperature, skin color, blood pressure, posture, or sweat levels. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein the patient monitoring queue is ranked based at least in part on the patient acuity measures. 
     
     
       3. The computer-implemented method of  claim 2 , wherein the one or more initial vital signs acquired from each patient are acquired with medical equipment that is different than the one or more vital sign acquisition cameras. 
     
     
       4. The computer-implemented method of  claim 2 , wherein the given patient is selected from the patient monitoring queue based on a position of the given patient in the patient monitoring queue. 
     
     
       5. The computer-implemented method of  claim 4 , further comprising altering, by one or more of the processors, a position of the given patient in the patient monitoring queue based at least in part on the updated patient acuity measure. 
     
     
       6. The computer-implemented method of  claim 1 , further comprising identifying, by one or more of the processors, the given patient among the plurality of patients in the area based on a reference image depicting the given patient. 
     
     
       7. The computer-implemented method of  claim 1 , wherein the area comprises a medical waiting room. 
     
     
       8. The computer-implemented method of  claim 1 , wherein the one or more vital sign acquisition cameras includes a pan-tilt-zoom (“PTZ”) camera. 
     
     
       9. A system comprising:
 one or more processors; 
 one or more vital sign acquisition cameras operably coupled with the one or more processors; and 
 memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to: 
 acquire one or more initial vital signs from each of a plurality of patients; 
 determine a patient acuity measure associated with each of the plurality of patients based on the acquired one or more vital signs from each of the plurality of patients; 
 establish a patient monitoring queue that includes the plurality of patients, the plurality of patients being located an area that can be captured by the one or more vital sign acquisition cameras; 
 unobtrusively acquire, by one or more of the vital sign acquisition cameras, one or more updated vital signs from a given patient selected from the patient monitoring queue; 
 determine an updated patient acuity measure for the given patient based on the one or more updated vital signs unobtrusively acquired by the one or more vital sign acquisition cameras from the given patient using a machine learning model; 
 detect, based on a comparison of the updated patient acuity measure for the given patient and the previously determined patient acuity measure for the given patient, deterioration of the given patient; 
 update the machine learning model by writing new trained weights for a least one module of the machine learning model; and 
 provide output alerting medical personnel of the deterioration of the given patient, 
 wherein the one or more vital signs cameras acquire the one or more updated vital signs by applying image detection algorithms, and 
 wherein the one or more updated vital signs comprise one or more of blood pressure, pulse, glucose level, SO 2 , photoplethysmogram, respiration rate, temperature, skin color, blood pressure, posture, or sweat levels. 
 
     
     
       10. The system of  claim 9 , wherein the plurality of patients are ranked in the patient monitoring queue based at least in part on the patient acuity measures. 
     
     
       11. The system of  claim 9 , wherein the given patient is selected from the patient monitoring queue based on a position of the given patient in the patient monitoring queue. 
     
     
       12. The system of  claim 9 , wherein the output comprises one or both of a reference image of the given patient and a location of the given patient in the area. 
     
     
       13. The system of  claim 10 , further comprising altering, by one or more of the processors, a position of the given patient in the patient monitoring queue based at least in part on the updated patient acuity measure. 
     
     
       14. The system of  claim 9 , further comprising identifying, by one or more of the processors, the given patient among the plurality of patients in the area based on a reference image depicting the given patient. 
     
     
       15. At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
 acquiring one or more initial vital signs from each of a plurality of patients; 
 determining a patient acuity measure associated with each of the plurality of patients based on the acquired one or more initial vital signs from each of the plurality of patients; 
 establishing a patient monitoring queue that includes the plurality of patients, the plurality of patient being located in an area that can be captured by one or more vital sign acquisition cameras; 
 unobtrusively acquiring, by one or more of the vital sign acquisition cameras, one or more updated vital signs from a given patient selected from the patient monitoring queue; 
 determining an updated patient acuity measure for the given patient based on the one or more updated vital signs unobtrusively acquired by the one or more vital sign acquisition cameras from the given patient using a machine learning model; 
 detecting, based on a comparison of the updated patient acuity measure for the given patient and the previously determined patient acuity measure for the given patient, deterioration of the given patient; 
 updating the machine learning model by writing new trained weights for at least one module of the machine learning model; and 
 providing output alerting medical personnel of the deterioration of the given patient, 
 wherein the one or more vital signs cameras acquire the one or more updated vital signs by applying image detection algorithms, and 
 wherein the one or more updated vital signs comprise one or more of blood pressure, pulse, glucose level, SO 2 , photoplethysmogram, respiration rate, temperature, skin color, blood pressure, posture, or sweat levels.